Sensing or determining the friction coefficient that acts between the tires and the road or detecting the condition of the road (e.g. dry, wet, snow-covered, and icy) from which the friction coefficient group can be derived is a major prerequisite to support the driver in his or her driving task and to avoid severe accidents. In general, assessment of the road conditions is the job of the driver, who will then adjust his or her driving style accordingly. Vehicle control systems such as ESC (Electronic Stability Control)/TCS (Traction Control System), or ABS (Anti-lock Braking System) help the driver to stabilize the vehicle in risky conditions, so that he or she will be better able to cope with driving in extreme situations.
Accident prevention is becoming increasingly important in driver assistance systems. Emergency braking and most recently also emergency collision avoidance systems are making an important contribution. But their effectiveness decisively depends on the friction coefficient of the ground. Moisture, snow, and ice reduce the coefficient of friction available between the tires and the road considerably compared to a dry road.
Document EP 792 228 B1 discloses a directional stability control system for ESP (Electronic Stability Program)/ESC controllers, which can be used in special situations to determine a friction coefficient. If at least one wheel utilizes the friction coefficient, e.g. when driving on a slippery road, the vehicle brake control system can determine the friction coefficient from the rotational behavior of the wheels and the ESP/ESC acceleration sensors.
Document DE 102 56 726 A1 discloses a method for generating a signal depending on road conditions using a reflection signal sensor, such as a radar or optical sensor. This facilitates proactive detection of the road condition in a motor vehicle.
Document DE 10 2004 018 088 A1 discloses a road recognition system having a temperature sensor, an ultrasonic sensor, and a camera. The road data obtained from the sensors is filtered, compared to reference data to determine whether the road is in drivable condition, in which process the type of road surface (e.g. concrete, asphalt, dirt, grass, sand, or gravel) and its condition (e.g. dry, icy, snow-covered, wet) can be classified.
Document DE 10 2004 047 914 A1 discloses a method for estimating the road condition in which data from multiple different sensors, such as camera, infrared sensor, rain sensor, or microphone, is merged to obtain a classification of the road condition to which a friction coefficient can be assigned.
It is further known that the friction coefficient information is not only output as driver information but also provided to other vehicle or driver assistance systems, so that these can adjust their operating state accordingly. For example, the ACC can be set to longer distances, or a curve warning function can be adjusted accordingly in case of a low friction coefficient.
Tire slip and tire vibration can be analyzed based on the wheel speed signal and then be used to classify the friction coefficient. The advantage is that this solution can be implemented as a pure software solution, which means cost-efficiently, in an electronic braking system (ESP). The disadvantage is that the friction coefficient cannot be determined proactively, but only after passing over the road surface.
Document DE 10 2008 047 750 A1 discloses a corresponding determination of traction using few sensors, in which torsional vibrations of a wheel of a vehicle are analyzed and a friction coefficient is estimated based on said analysis.
Document DE 10 2009 041 566 A1 discloses a method for determining a road friction coefficient μ in which a first constantly updated friction coefficient characteristic and a second friction coefficient variable that is only updated depending on the situation are combined into a joint estimated friction coefficient.
Document WO 2011/007015 A1 discloses a laser-based method for friction coefficient classification in motor vehicles.
Signals of a LiDAR or CV sensor aimed at the road surface are analyzed and subsequently friction coefficients are assigned based on the amplitude of the measured road surface. It can be estimated, for example, if snow, asphalt, or ice make up the road surface.
It is further known that images provided by one or several camera(s) in a vehicle can be interpreted in such a manner that conclusions with respect to the road surface can be drawn (e.g. based on reflections and brightness levels), which can also be used for friction coefficient classification. Since surroundings cameras are becoming more and more common in driver assistance systems (e.g. for detecting lane departures, traffic signs, and objects), this solution can also be provided cost-efficiently as a software add-on. It has the advantage that the friction coefficient can be estimated proactively. The disadvantage is that this method does not allow consistently precise interpretation because interferences (other vehicles, light sources, etc.) may have a negative effect on interpretation and result in misinterpretation.
Document WO 2012/110030 A2 discloses a method and an apparatus for estimating the friction coefficient using a 3D camera, such as a stereo camera. At least one image of the vehicle environment is taken using the 3D camera. The image data of the 3D camera is used to create a height profile of the road surface in the entire area in front of the vehicle. The anticipated local coefficient of friction of the road surface in the area in front of the vehicle is estimated from said height profile.
The approaches based on camera or tire speed signal evaluations described above have the disadvantages described there.